#!/usr/bin/env python
# -*- coding: utf-8 -*-

""" By Martin Senande-Rivera
    For Towards and atmosphere more favourable to firestorm development in Europe """

import os
import glob, sys
import numpy as np
import xarray as xr
import pandas as pd
from scipy import stats

path_fwi='../0-DATA/FWI/'
path_outs='./ERA5/'

from dask_mpi import initialize
initialize()

from distributed import Client
client = Client()

# Read files
fwis=sorted(glob.glob(path_fwi+'ERA5_*.nc'))
FWI = xr.open_mfdataset(fwis,combine='by_coords')['fwi'].chunk({'time':-1,'latitude': 15, 'longitude': 15})
FWI_thres = xr.open_dataset(path_outs+'FWI_90threshold.nc')['fwi']

num = xr.full_like(FWI, 0).chunk({'time':-1,'latitude': 15, 'longitude': 15}) # Daily array full of 0
num = xr.where(FWI>FWI_thres,1,num)                                           # Days with FWI above the threshold are classified as 1
ndays = num.chunk({'time':-1,'latitude': 15, 'longitude': 15}).resample(time='1Y').sum(dim='time') # Number of days with FWI above the threshold
ndays.chunk({'time':-1,'latitude': 15, 'longitude': 15}).to_netcdf(path_outs+'FWI_ndays.nc')       # Save output

ndays_p = ndays.sel(time=slice('1980-01-01','2018-12-31')).chunk({'time':-1,'latitude': 15, 'longitude': 15}) # Select the 1980-2018 period
ndays_present = ndays_p.sum(dim='time')/39.                # Average number of days per year with FWI above the threshold in the 1980-2018 period
ndays_present.to_netcdf(path_outs+'FWI_ndays-present.nc')  # Save output
